{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.tools.pivot import pivot_table\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels as sm\n",
    "from scipy.stats import zscore \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "\n",
    "import pandas.rpy.common as com \n",
    "import rpy2.robjects as robjects\n",
    "from rpy2.robjects.packages import importr\n",
    "import rpy2\n",
    "texreg = importr('texreg')\n",
    " \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_eci = pd.read_csv('../data/ECI data/eci_all_years.csv')\n",
    "df = pd.read_csv('../data/country_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year_selected = \"2005\"\n",
    "\n",
    "##select specific columns\n",
    "col_selected = ['code','country']\n",
    "for col in df.columns:\n",
    "    #print col[-2:]\n",
    "    if col[-2:] == year_selected[-2:]:\n",
    "        col_selected.append(col)\n",
    "df = df.ix[:,col_selected]\n",
    "df['years_of_education_'+year_selected] = df['years_of_secondary_education_'+year_selected]+ df['years_of_primary_education_'+year_selected]\n",
    "\n",
    "##select specific eci year\n",
    "eci_2005 = all_eci[all_eci.year == float(year_selected) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = pd.merge(df, eci_2005, how='left', left_on='code',right_on='code')\n",
    "X = X[np.isfinite(X['eci'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cant do it with  code\n",
      "cant do it with  country_x\n",
      "cant do it with  country_y\n"
     ]
    }
   ],
   "source": [
    "X['population_'+year_selected] = np.log(X['population_'+year_selected])\n",
    "X['gdp_ppp_'+year_selected] = np.log(X['gdp_ppp_'+year_selected])\n",
    "\n",
    "##standarize\n",
    "for c in X.columns:\n",
    "    try:\n",
    "        X[c] = (X[c] - X[c].mean())/X[c].std(ddof=0)\n",
    "    except:\n",
    "        print 'cant do it with ',c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'code', u'country_x', u'gdp_ppp_2005', u'population_2005', u'life_expectency_2005', u'years_of_primary_education_2005', u'years_of_secondary_education_2005', u'laborForce_patricipation_2005', u'gross_savings_percent_2005', u'inflation_GDP_deflator_2005', u'inflation_consumer_prices_2005', u'industry_value_added_2005', u'exports_of_goods_and_services_2005', u'agriculture_value_added_2005', u'tertiary_education_2005', u'natural_resources_rents_2005', u'gini_ppp_2005', u'years_of_education_2005', u'year', u'rank', u'country_y', u'eci'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tertiary_education_2005+industry_value_added_2005+exports_of_goods_and_services_2005+natural_resources_rents_2005+population_2005\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    eci   R-squared:                       0.726\n",
      "Model:                            OLS   Adj. R-squared:                  0.711\n",
      "Method:                 Least Squares   F-statistic:                     47.24\n",
      "Date:                Sun, 03 May 2015   Prob (F-statistic):           1.32e-23\n",
      "Time:                        15:27:06   Log-Likelihood:                -74.866\n",
      "No. Observations:                  95   AIC:                             161.7\n",
      "Df Residuals:                      89   BIC:                             177.1\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "Intercept                              0.0523      0.058      0.908      0.366        -0.062     0.167\n",
      "tertiary_education_2005                0.6375      0.061     10.461      0.000         0.516     0.759\n",
      "industry_value_added_2005              0.2753      0.098      2.809      0.006         0.081     0.470\n",
      "exports_of_goods_and_services_2005     0.2479      0.071      3.472      0.001         0.106     0.390\n",
      "natural_resources_rents_2005          -0.6000      0.104     -5.750      0.000        -0.807    -0.393\n",
      "population_2005                        0.2053      0.058      3.556      0.001         0.091     0.320\n",
      "==============================================================================\n",
      "Omnibus:                        8.106   Durbin-Watson:                   1.732\n",
      "Prob(Omnibus):                  0.017   Jarque-Bera (JB):                8.475\n",
      "Skew:                           0.521   Prob(JB):                       0.0144\n",
      "Kurtosis:                       4.027   Cond. No.                         2.96\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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IHLKKvswCZrX9/unAnA67S5KkVXjYpustv0Gx0zZJHc3IzLntjbVzqi8FHhUR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\ncNJrwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102d0a410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Index([u'code', u'country', u'gdp_ppp_2005', u'population_2005', \n",
    "#u'life_expectency_2005', u'years_of_primary_education_2005', u'years_of_secondary_education_2005',\n",
    "#u'laborForce_patricipation_2005', u'gross_savings_percent_2005', u'inflation_GDP_deflator_2005', \n",
    "#u'inflation_consumer_prices_2005', u'industry_value_added_2005', u'exports_of_goods_and_services_2005', \n",
    "#u'years_of_education_2005', u'Year', u'Rank', u'Abbrv', u'Country', u'ECI'], dtype='object')\n",
    "\n",
    "independent_vars = ['tertiary_education','industry_value_added','exports_of_goods_and_services',\n",
    "                    'natural_resources_rents','population']\n",
    "\n",
    "independent_vars = ('_'+year_selected+'+').join(independent_vars) + '_'+year_selected\n",
    "print independent_vars\n",
    "#assert False\n",
    "formula=\"eci ~\"+independent_vars\n",
    "\n",
    "\n",
    "\n",
    "#result = smf.mixedlm(formula=formula, groups=\"code\", data=X).fit()\n",
    "result = smf.ols(formula=formula, data=X).fit()\n",
    "\n",
    "print result.summary()\n",
    "\n",
    "\n",
    "sns.set_style('ticks')\n",
    "\n",
    "\n",
    "sns.coefplot(formula=formula, data=X, palette='muted',ci=90)\n",
    "\n",
    "fig = sns.plt.gcf()\n",
    "ax = sns.plt.gca()\n",
    "\n",
    "fig.set_size_inches((12,8))\n",
    "\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(15)\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(15) \n",
    "\n",
    "plt.savefig('coefplot.pdf', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfr = com.convert_to_r_dataframe(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rformula = robjects.Formula(formula) \n",
    "\n",
    "model = robjects.r.lm(formula=rformula,data=dfr)\n",
    "\n",
    "latex = texreg.texreg(model, single_row=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\begin{table}\n",
      "\\begin{center}\n",
      "\\begin{tabular}{l c }\n",
      "\\hline\n",
      "                                        & Model 1 \\\\\n",
      "\\hline\n",
      "(Intercept)                             & $0.05 \\; (0.06)$        \\\\\n",
      "tertiary\\_education\\_2005               & $0.64 \\; (0.06)^{***}$  \\\\\n",
      "industry\\_value\\_added\\_2005            & $0.28 \\; (0.10)^{**}$   \\\\\n",
      "exports\\_of\\_goods\\_and\\_services\\_2005 & $0.25 \\; (0.07)^{***}$  \\\\\n",
      "natural\\_resources\\_rents\\_2005         & $-0.60 \\; (0.10)^{***}$ \\\\\n",
      "population\\_2005                        & $0.21 \\; (0.06)^{***}$  \\\\\n",
      "\\hline\n",
      "R$^2$                                   & 0.73                    \\\\\n",
      "Adj. R$^2$                              & 0.71                    \\\\\n",
      "Num. obs.                               & 95                      \\\\\n",
      "\\hline\n",
      "\\multicolumn{2}{l}{\\scriptsize{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}\n",
      "\\end{tabular}\n",
      "\\caption{Statistical models}\n",
      "\\label{table:coefficients}\n",
      "\\end{center}\n",
      "\\end{table}\n",
      " FALSE\n"
     ]
    }
   ],
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    "print latex"
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   "outputs": [],
   "source": []
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